Conditional Tail Expectation Decomposition and Conditional Mean Risk Sharing for Dependent and Conditionally Independent Losses

Michel Denuit () and Christian Y. Robert ()
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Michel Denuit: Institute of Statistics, Biostatistics and Actuarial Science - ISBA Louvain Institute of Data Analysis and Modeling - LIDAM UCLouvain
Christian Y. Robert: Laboratory in Finance and Insurance - LFA CREST - Center for Research in Economics and Statistics ENSAE

Methodology and Computing in Applied Probability, 2022, vol. 24, issue 3, 1953-1985

Abstract: Abstract Conditional tail expectations are often used in risk measurement and capital allocation. Conditional mean risk sharing appears to be effective in collaborative insurance, to distribute total losses among participants. This paper develops analytical results for risk allocation among different, correlated units based on conditional tail expectations and conditional mean risk sharing. Results available in the literature for independent risks are extended to correlated ones, in a unified way. The approach is applied to mixture models with correlated latent factors that are often used in practice. Conditional Monte Carlo simulation procedures are proposed in that setting.

Keywords: Weighted distributions; Size-biased transform; Mixture models; Archimedean copulas; Conditional Monte Carlo simulation; 62P05 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11009-021-09888-0

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